Within this application several publications are referenced by Arabic numerals within brackets. Full citations for these and other publications may be found at the end of the specification immediately preceding the claims. The disclosures of all these publications in their entireties are hereby expressly incorporated by reference into the present application for the purposes of indicating the background of the present invention and illustrating the state of the art.
An organ transplant surgery replaces a failing organ with a healthy organ. The success rates of transplant surgery have improved remarkably from its start, but growing shortages exist in the supply of organs and tissues available for transplantation. Organs and tissues that are transplanted within the same person's body are called autografts. Transplants that are performed between two subjects of the same species are called allografts. Allografts can either be from a living or cadaveric source.
The immune system is capable of discerning a cell as being ‘self’ or ‘non-self’ according to that cell's serotype. In humans, that serotype is largely determined by human leukocyte antigen (HLA), the human version of the major histocompatibility complex. Cells determined to be non-self are usually recognized by the immune system as foreign, resulting in an immune response. Serotypes differ widely between individuals. Therefore, if organ from one human are introduced into another human, the organ is oftentimes determined to be non-self because it do not match the self-serotype, and rejected by the recipient's immune system. Critical decisions must be made prior to organ transplantation to appropriately match donors and recipients.
In some situation, this rejection can be reduced by serotyping both recipient and potential donors to determine the closest HLA match. The United Network for Organ Sharing (UNOS) facilitates organ sharing for transplantation using policies developed by a committee of experts, and approved by the Secretary of Health and Human Services. These policies define the criteria by organ type for organ matching, procurement, and distribution. (http://optn.transplant.hrsa.gov/policiesAndBylaws/policies.asp). For example, in kidney transplantation cases, the matching system allocates organs based on time on list, human leukocyte antigen (HLA A locus, B locus and DR locus) matching, and whether recipient is suitable for an extended criteria donor (ECD) kidney. However, the tools currently used to make final allocation decisions are inadequate and subjective, which may result in sub-optimal graft survival. Acute rejection of the graft by the host's immune system remains an unsolved problem in allograft organ transplant. Immunosuppressive drugs are used to help to prevent and manage acute rejection episodes in many situations.
However, as in the case of kidney transplantation, the expansion of the wait list far exceeds the number of available donor organs, contributing to the stress on the allocation system. In 2007, approximately 72,000 patients were listed with the United Network for Organ Sharing (UNOS), with only 17,513 receiving transplants, which was a 3% decrease over the previous year. Of those patients transplanted with deceased-donor grafts, approximately 10% of the grafts will fail in the first year with an additional 32% failing at five years and 61% at 10 years, which would return those patients to the wait list. In an effort to bridge this gap, medical professionals are relying on extended criteria donors (ECD) as well as donation after cardiac death (DCD). With the use of a greater number of these grafts, the ability to accurately predict graft failure becomes increasingly critical to maximize donation to the most suitable recipient and to minimize the flow of patients returning to the already burdened wait list. This application describes an objective tool that transplant surgeon may use for pairing donor organs with appropriate recipients to optimize outcomes.
As evidence-based medicine is becoming the standard of care, clinicians look towards prognostic tools to assist in decision making. [3] Machine learning can enable the development of a predictive model that incorporates multiple variables for a systems approach to organ allocation. Nomograms, neural networks, and decision trees have become popular methods for creating more objective ways to predict transplant outcomes [3, 5, 9-11]. While there are several publications on various models to predict allograft survival, these models rely on either both pre- and post-operative variables, or use only a handful of pre-operative variables for model functionality. Some of the models including nomograms, neural networks and tree-modeling offer positive predictive values for graft survival of 43.5%, 82.1% and 76%. [3-5] However, these models have not yet been implemented routinely in clinical setting.
Bayesian statistics is well suited to the analysis of large numbers of variables to predict outcomes. Originally developed in the 18th century, advances in computing power have made it practical today. Bayesian methodology has been used to predict survival in liver transplant patients, whereby using pre-transplant variables, the authors were able to predict 90-day survival with a positive predictive value (PPV) of 91% and an area under the curve (AUC) of 0.681. [6] The Bayesian modeling approach has not yet been applied to outcome prediction in renal allograft surgeries. Unlike traditional or frequentist statistical methods, Bayesian statistics lends itself to use with large databases, can tolerate missing values and incomplete variables, and can graphically describe the probability distributions of outcomes [7]. In other words, this type of statistical analysis allows for the use of an unlimited number of variables, and not only shows the relationship between each variable and the targeted outcome, but also the contribution of inter-variable relationships to the probability of each outcome.
This disclosure describes a machine-learning tool to generate a minimized Bayesian network that accurately predicts graft failure one and three years after transplantation based solely on pre-operative variables.